Ohio State Innovation Foundation

Siemens Medical Systems, Inc.

The Ohio State University

Other

Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Healthcare Gmbh

Siemens Corporation

Orlando P Simonetti

Yu Ding

Rizwan Ahmad

Ning Jin

Lee C Potter

Adam Rich

Yiu-Cho Chung

Jeffrey M Bundy

Marie-Pierre Jolly

Hui Xue

J Paul Finn

Samuel T Ting

Qiang Zhang

Berthold Kiefer

Raymond J Kim

Gerhard Laub

Sven Zuehlsdorff

Christoph Guetter

Jens Guehring

Arne Littmann

David Steven Gross

Randall Kroeker

Robert M Judd

Richard D White

Subha Raman

Yingmin Liu

Andrew C Larson

Vidhya Kumar

Samuel Tze Luong Ting

Jeffrey Crompton

Prashanth Palaniappan

Kyle Koppenhoefer

Hung-Yu Lin

Jacob A Bender

Juliet Jaison Varghese

Joshua Ash

Jiamin Wang

A variable density Cartesian sampling method that allows retrospective adjustment of temporal resolution, providing added flexibility for real-time applications where optimal temporal resolution may not be known in advance. The methods provide for a computationally efficient sampling methods where a first step includes producing a uniformly random sampling pattern using a golden ratio on a grid, and the second step is applying a nonlinear stretching operation to create a variable density sampling pattern. Diagnostic quality images may be recovered at different temporal resolutions.

Cartesian sampling for dynamic magnetic resonance imaging (MRI)

Methods for data acquisition and processing of magnetic resonance (MR) imaging to obtain the oxygen saturation (O2sat) of blood using a relationship between transverse relaxation time (T2) of blood and oxygen saturation. The method includes obtaining multiple images at various T2 preparation times. Next, non-linear curve fitting may be used to solve for arterial or venous O2sat. The disclosure provides a calibration-free method for accurate quantitative assessment of blood in the heart and deep vessels, even in locations having limited accessibility with other diagnostic techniques.

Magnetic resonance imaging method to non-invasively measure blood oxygen saturation

In some aspects, the present disclosure relates to a method for non-invasively assessing a myocardial region of a subject by computed tomography (CT). In one embodiment, the method comprises: acquiring non-contrast imaging data for a myocardial region of a subject using dual energy computed tomography (DECT) scanning; reconstructing, from the acquired non-contrast imaging data, monochromatic images for a plurality of energy levels in a range of energy levels; determining, based at least in part on the image reconstruction, attenuation values for each respective energy level of the plurality of energy levels; and performing at least one of detection and quantification of myocardial fibrosis based at least in part on differences in the attenuation values across the plurality of energy levels.

Assessing a condition of a subject using non-contrast dual energy computed tomography

Disclosed is a method for determining, among other things, the temperature profile of a medical implant in a patient when subjected to an MRI scan or machine, thus enabling a determination of the risk of temperature induced tissue necrosis or damage to the implant. The specific position of the implant in the patient changes the temperature dispersion in the body and is accounted for in the creation of the temperature profile. The method includes mapping with an imaging unit location, size and orientation of the medical implant in a patient, and storing the location, size and orientation in a mapped data. Then, translating the data to a model patient of gender, age, weight, height, and body structure of the patient with a model medical implant. Further, determining the parameters of an MRI unit to be used and computing the temperature profile of the implant to ascertain temperature impact.

Precision medicine approach to improving patient safety and access to MRI

Methods and systems for accelerated Phase-contrast magnetic resonance imaging (PC-MRI). The technique is based on Bayesian inference and provides for fast computation via an approximate message passing algorithm. The Bayesian formulation allows modeling and exploitation of the statistical relationships across space, time, and encodings in order to achieve reproducible estimation of flow from highly undersampled data.

Bayesian model for highly accelerated phase-contrast MRI

A low-cost magnetic resonance imaging (MRI) apparatus that may be used for, e.g., Cardiac MRI (CMR). CMR is a clinical imaging tool that can investigate a wide range of cardiac conditions by exploring changes in the anatomy, physiology, or dynamics of the heart. The applications of CMR include, but are not limited to, the evaluation of congenital heart disease, coronary heart disease, ischemic heart disease, cardiac masses, the pericardium, cardiomyopathy, hibernating myocardium, and valvular and ventricular function. Generally, the MRI apparatus is a low-field (&#x2dc;0.5T) system equipped with a high-end data processing unit (DPU) to enable the implementation of structure aware (SA) recovery in clinically relevant times.

Low field magnetic resonance imaging (MRI) scanner for cardiac imaging

MRI techniques seek to simultaneously measure physiological parameters in multiple directions, requiring the application of multiple encoding magnetic field gradient waveforms. The use of multiple encoding waveforms degrades the temporal resolution of the measurement, or may distort the results depending on the methodology used to derive physiological parameters from the measured data. The disclosed Direct Inversion Reconstruction Method (DiR) provides distortion-free velocity images with high temporal resolution, without changing the method of acquiring the phase data. The disclosed method provides a more stable and accurate recovery of phase-based dynamic magnetic resonance signals with higher temporal resolution than current state-of-the-art methods.

Direct inversion for phase-based dynamic magnetic resonance measurements

A method for improving the signal-to-noise ratio (SNR) of TGRAPPA. The SNR of the ACS lines is proportional to the condition number of the GRAPPA kernel encoding equations. Therefore, the GRAPPA kernel estimated from higher SNR ACS lines amplifies the random noise in GRAPPA reconstruction. In TGRAPPA reconstruction of dynamic image series, a widely used method to acquire ACS lines is to average-all-frame (AAF). The present disclosure utilizes a tile-all-frame (TAF) as ACS lines to improve the SNR of the reconstructed images.

Method for estimating a GRAPPA reconstruction kernel

A novel free-breathing myocardial T2* mapping combining multiple single-shot black-blood GRE-EPI images with automatic non-rigid motion correction. The present disclosure describes a method of accurate myocardial T2* measurements that is insensitive to respiratory motion, and is likely to reduce sensitivity to arrhythmia as well since each image is acquired in a single heart beat. The T2*-weighted GRE-EPI images are motion corrected using, e.g., automatic non-rigid motion correction to reduce mis-registration due to respiratory motion. A T2* map is calculated using the motion-corrected T2*-weighted images by fitting pixel intensities to a, e.g., two-parameter mono-exponential model.

Free-breathing myocardial T2* mapping using single-heartbeat, multi-shot, gradient echo-echo planar imaging (GRE-EPI) and automatic non-rigid motion correction

A pseudo-random, incoherent sampling technique, called Variable density Incoherent Spatiotemporal Acquisition (VISTA) is disclosed, which is based on minimal Riesz energy problem. Compared with other pseudorandom methods (e.g., PDS), VISTA has the unique ability to incorporate a variety of problem-specific constraints. In this study, VISTA was applied to real-time CMR, where it not only provided an incoherent sampling with variable density but also ensured a constant temporal resolution and a fully sampled time-averaged data.

Variable density incoherent spatiotemporal acquisition (VISTA) for highly accelerated magnetic resonance imaging

Systems and methods of correcting eddy current-induced background phase (EC-BP) in magnetic resonance imaging (PC-MRI) data. The method includes acquiring a slice of interest (SOI) at a first table position using a magnetic resonance imaging (MRI) scanner, the slice of interest having a predetermined imaging orientation and being acquired having predetermined gradient waveforms; acquiring at least one additional slice at a second table position using the MRI scanner, the at least one additional slice having a same imaging orientation as the slice of interest and being acquired using the same gradient waveforms as the slice of interest; determining time-averaged phase maps from the slice of interest and the at least one additional slice; determining a correction map from the time-averaged phase maps; and correcting a background phase (BP) of the slice of interest using the correction map.

Background phase correction for quantitative cardiovascular MRI

A low-cost magnetic resonance imaging (MRI) apparatus that may be used for, e.g., Cardiac MRI (CMR). CMR is a clinical imaging tool that can investigate a wide range of cardiac conditions by exploring changes in the anatomy, physiology, or dynamics of the heart. The applications of CMR include, but are not limited to, the evaluation of congenital heart disease, coronary heart disease, ischemic heart disease, cardiac masses, the pericardium, cardiomyopathy, hibernating myocardium, and valvular and ventricular function. Generally, the MRI apparatus is a low-field (0.5 T) system equipped with a high-end data processing unit (DPU) to enable the implementation of structure aware (SA) recovery in clinically relevant times.

Magnetic resonance reconstruction includes motion compensation. Inverse-consistent non-rigid registration is used to determine motion between shots. The motion is incorporated into reconstruction. The incorporation compensates for the motion resulting from the period over which the MR data is acquired.

Motion compensated magnetic resonance reconstruction in real-time imaging

Parallel magnetic resonance imaging (pMRI) reconstruction techniques are commonly used to reduce scan time by undersampling the k-space data. In GRAPPA, a k-space based pMRI technique, the missing k-space data are estimated by solving a set of linear equations; however, this set of equations does not take advantage of the correlations within the missing k-space data. All k-space data in a neighborhood acquired from a phased-array coil are correlated. The correlation can be estimated easily as a self-constraint condition, and formulated as an extra set of linear equations to improve the performance of GRAPPA. We propose a modified k-space based pMRI technique call self-constraint GRAPPA (SC-GRAPPA) which combines the linear equations of GRAPPA with these extra equations to solve for the missing k-space data. Since SC-GRAPPA utilizes a least-squares solution of the linear equations, it has a closed-form solution that does not require an iterative solver.

Self-constraint non-iterative GRAPPA reconstruction with closed-form solution

A hybrid filtering method called Karhunen Loeve Transform-Wavelet (KW) filtering is presented to de-noise dynamic cardiac magnetic resonance images that simultaneously takes advantage of the intrinsic spatial and temporal redundancies of real-time cardiac cine. This filtering technique combines a temporal Karhunen-Loeve transform (KLT) and spatial adaptive wavelet filtering. KW filtering has four steps. The first is applying the KLT along the temporal direction, generating a series of 'eigenimages'. The second is applying Marcenko-Pastur (MP) law to identify and discard the noise-only eigenimages. The third applying a 2-D spatial wavelet filter with adaptive threshold to each eigenimage to define the wavelet filter strength for each of the eigenimages based on the noise variance and standard deviation of the signal. Lastly, the inverse KLT is applied to the filtered eigenimages to generate a new series of cine images with reduced image noise.

Growing community of inventors

Naperville, IL, United States of America

Orlando P Simonetti